{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 基于Faster RCNN的枪支检测(PaddleX快速上手版)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 项目简介：\n",
    "在平日的生活娱乐场所中存在着诸多安全隐患，其中出售违规枪支、手持枪支抢劫的犯罪情形危害性极大。通过计算机视觉的方法对交通运输、社交娱乐等重点活动场所进行监控，识别出可能存在出售违规枪支、持握枪支的场景，实现预防早期地恶性犯罪事件、降低财产损失、保护人民的生命健康、在公安机关打击有关违法犯罪上至关重要。计算能力的提高、存储设备的发展，使得传统视觉技术中存在的问题逐渐得到改善或解决。本次项目初步使用PaddleX进行快速训练，后期将对其进行其他方面的优化。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 目录\n",
    "1. PaddleX工具简介与安装\n",
    "1. 数据集预处理\t\n",
    "1. 配置超参数并训练模型\n",
    "1. 测试模型效果\n",
    "1. 可视化模型效果\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 一、PaddleX工具简介与安装\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/d8e24364f665432e810d3a681fd814ec7dfa6492dfee4d28aee86313b9898e7b)\n",
    "\n",
    "本文使用的框架是PaddleX，PaddleX是飞桨全流程开发工具，集飞桨核心框架、模型库、工具及组件等深度学习开发所需全部能力于一身，集成飞桨智能视觉领域图像分类、目标检测、语义分割、实例分割任务能力，将深度学习开发全流程从数据准备、模型训练与优化到多端部署端到端打通，并提供统一任务API接口及图形化开发界面Demo。\n",
    "\n",
    "PaddleX代码GitHub链接：https://github.com/PaddlePaddle/PaddleX/tree/develop\n",
    "\n",
    "PaddleX文档链接：https://paddlex.readthedocs.io/zh_CN/latest/index.html\n",
    "\n",
    "PaddleX官网链接：https://www.paddlepaddle.org.cn/paddle/paddlex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 1.1解压数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!unzip data/data128197/guns.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 1.2PaddleX安装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!pip install paddlex==2.0.0rc4\r\n",
    "!pip install lxml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 二、数据集预处理\n",
    "\n",
    "1. 数据集包括图像文件和标注文件，用于对象检测神经网络的训练和验证。数据集是由各个网络上的视频与图片裁剪而成。\n",
    "1. 清洗目标把数据集转换成VOC格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#将txt格式的标注文件转为xml格式\r\n",
    "import os,shutil\r\n",
    "import cv2\r\n",
    "from lxml.etree import Element, SubElement, tostring\r\n",
    "def txt_xml(img_path,img_name,txt_path,img_txt,xml_path,img_xml):\r\n",
    "    #读取txt的信息\r\n",
    "    clas=[]\r\n",
    "    img=cv2.imread(os.path.join(img_path,img_name))\r\n",
    "    imh, imw = img.shape[0:2]\r\n",
    "    txt_img=os.path.join(txt_path,img_txt)\r\n",
    "    with open(txt_img,\"r\") as f:\r\n",
    "        next(f)\r\n",
    "        for line in f.readlines():\r\n",
    "            line = line.strip('\\n')\r\n",
    "            list = line.split(\" \")\r\n",
    "            print(list)\r\n",
    "            clas.append(list)\r\n",
    "    node_root = Element('annotation')\r\n",
    "    node_folder = SubElement(node_root, 'folder')\r\n",
    "    node_folder.text = '1'\r\n",
    "    node_filename = SubElement(node_root, 'filename')\r\n",
    "    #图像名称\r\n",
    "    node_filename.text = img_name\r\n",
    "    node_size = SubElement(node_root, 'size')\r\n",
    "    node_width = SubElement(node_size, 'width')\r\n",
    "    node_width.text = str(imw)\r\n",
    "    node_height = SubElement(node_size, 'height')\r\n",
    "    node_height.text = str(imh)\r\n",
    "    node_depth = SubElement(node_size, 'depth')\r\n",
    "    node_depth.text = '3'\r\n",
    "    for i in range(len(clas)):\r\n",
    "        node_object = SubElement(node_root, 'object')\r\n",
    "        node_name = SubElement(node_object, 'name')\r\n",
    "        node_name.text = \"gun\"\r\n",
    "        node_pose=SubElement(node_object, 'pose')\r\n",
    "        node_pose.text=\"Unspecified\"\r\n",
    "        node_truncated=SubElement(node_object, 'truncated')\r\n",
    "        node_truncated.text=\"0\"\r\n",
    "        node_difficult = SubElement(node_object, 'difficult')\r\n",
    "        node_difficult.text = '0'\r\n",
    "        node_bndbox = SubElement(node_object, 'bndbox')\r\n",
    "        node_xmin = SubElement(node_bndbox, 'xmin')\r\n",
    "        node_xmin.text = str(clas[i][0])\r\n",
    "        node_ymin = SubElement(node_bndbox, 'ymin')\r\n",
    "        node_ymin.text = str(clas[i][1])\r\n",
    "        node_xmax = SubElement(node_bndbox, 'xmax')\r\n",
    "        node_xmax.text = str(clas[i][2])\r\n",
    "        node_ymax = SubElement(node_bndbox, 'ymax')\r\n",
    "        node_ymax.text = str(clas[i][3])\r\n",
    "    xml = tostring(node_root, pretty_print=True)  # 格式化显示，该换行的换行\r\n",
    "    img_newxml = os.path.join(xml_path, img_xml)\r\n",
    "    file_object = open(img_newxml, 'wb')\r\n",
    "    file_object.write(xml)\r\n",
    "    file_object.close()\r\n",
    "\r\n",
    "if __name__ == \"__main__\":\r\n",
    "    #图像文件夹所在位置\r\n",
    "    img_path = r\"guns/Images\"\r\n",
    "    #标注文件夹所在位置\r\n",
    "    txt_path=r\"guns/Labels\"\r\n",
    "    #txt转化成xml格式后存放的文件夹\r\n",
    "    xml_path=r\"VOC/Annotations\"\r\n",
    "    for img_name in os.listdir(img_path):\r\n",
    "        print(img_name)\r\n",
    "        img_xml=img_name.split(\".\")[0]+\".xml\"\r\n",
    "        img_txt=img_name.split(\".jpeg\")[0]+\".txt\"\r\n",
    "        txt_xml(img_path, img_name, txt_path, img_txt,xml_path, img_xml)\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 2.1可视化原图像标注情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 51840x51840 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\r\n",
    "import xml.dom.minidom\r\n",
    "import cv2 as cv\r\n",
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import matplotlib.patches as patches\r\n",
    "from matplotlib.image import imread\r\n",
    "import math\r\n",
    "ImgPath = \"/home/aistudio/VOC/JPEGImages/\"\r\n",
    "AnnoPath = \"/home/aistudio/VOC/Annotations/\" # xml文件地址\r\n",
    "\r\n",
    "def draw_rectangle(currentAxis, bbox, edgecolor = 'k', facecolor = 'y', fill=False, linestyle='-'):\r\n",
    "    # currentAxis，坐标轴，通过plt.gca()获取\r\n",
    "    # bbox，边界框，包含四个数值的list， [x1, y1, x2, y2]\r\n",
    "    # edgecolor，边框线条颜色\r\n",
    "    # facecolor，填充颜色\r\n",
    "    # fill, 是否填充\r\n",
    "    # linestype，边框线型\r\n",
    "    # patches.Rectangle需要传入左上角坐标、矩形区域的宽度、高度等参数\r\n",
    "    rect=patches.Rectangle((bbox[0], bbox[1]), bbox[2]-bbox[0]+1, bbox[3]-bbox[1]+1, linewidth=1,\r\n",
    "                           edgecolor=edgecolor,facecolor=facecolor,fill=fill, linestyle=linestyle)\r\n",
    "    currentAxis.add_patch(rect)\r\n",
    "\r\n",
    "image_pre='260'\r\n",
    "imgfile = ImgPath + image_pre + '.jpeg'\r\n",
    "xmlfile = AnnoPath + image_pre + '.xml'\r\n",
    "\r\n",
    "im = cv.imread(imgfile)\r\n",
    "plt.imshow(im)\r\n",
    "\r\n",
    "currentAxis=plt.gca()\r\n",
    "# 打开xml文档\r\n",
    "DOMTree = xml.dom.minidom.parse(xmlfile)\r\n",
    "# 得到文档元素对象\r\n",
    "collection = DOMTree.documentElement\r\n",
    "\r\n",
    "filenamelist = collection.getElementsByTagName(\"filename\")\r\n",
    "filename = filenamelist[0].childNodes[0].data\r\n",
    "\r\n",
    "# 得到标签名为object的信息\r\n",
    "objectlist = collection.getElementsByTagName(\"object\")\r\n",
    "\r\n",
    "for objects in objectlist:\r\n",
    "    # 每个object中得到子标签名为name的信息\r\n",
    "    namelist = objects.getElementsByTagName('name')\r\n",
    "    # 通过此语句得到具体的某个name的值\r\n",
    "    objectname = namelist[0].childNodes[0].data\r\n",
    "    #print(objectname)\r\n",
    "\r\n",
    "    bndbox = objects.getElementsByTagName('bndbox')\r\n",
    "    #print(bndbox)\r\n",
    "    \r\n",
    "    for box in bndbox:\r\n",
    "        x1_list = box.getElementsByTagName('xmin')\r\n",
    "        x1 = int(x1_list[0].childNodes[0].data)\r\n",
    "        y1_list = box.getElementsByTagName('ymin')\r\n",
    "        y1 = int(y1_list[0].childNodes[0].data)\r\n",
    "        x2_list = box.getElementsByTagName('xmax')  \r\n",
    "        x2 = int(x2_list[0].childNodes[0].data)\r\n",
    "        y2_list = box.getElementsByTagName('ymax')\r\n",
    "        y2 = int(y2_list[0].childNodes[0].data)\r\n",
    "        bbox=[x1, y1, x2, y2]\r\n",
    "        draw_rectangle(currentAxis, bbox, edgecolor = 'b')\r\n",
    "\r\n",
    "plt.axis('off')  \r\n",
    "plt.figure(figsize=(720, 720))      \r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 设置使用0号GPU卡\r\n",
    "import matplotlib\r\n",
    "matplotlib.use('Agg') \r\n",
    "import os\r\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\r\n",
    "import paddlex as pdx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 2.2使用PaddleX划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!paddlex --split_dataset --format VOC --dataset_dir VOC --val_value 0.2 --test_value 0.1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 2.3数据集配置与数据增强\n",
    "定义数据处理流程，其中训练和测试需分别定义，训练过程包括了部分测试过程中不需要的数据增强操作，如在本示例中，训练过程使用了MixupImage、RandomDistort、RandomExpand、RandomCrop、RandomHorizontalFlip和BatchRandomResize共6种数据增强方式\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from paddlex import transforms as T\r\n",
    "import paddlex as pdx\r\n",
    "train_transforms = T.Compose([\r\n",
    "    T.MixupImage(mixup_epoch=-1), T.RandomDistort(),\r\n",
    "    T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),\r\n",
    "    T.RandomHorizontalFlip(), T.BatchRandomResize(\r\n",
    "        target_sizes=[\r\n",
    "            320, 352, 384, 416, 448, 480, 512, 544, 576, 608, 640, 672, 704,\r\n",
    "            736, 768\r\n",
    "        ],\r\n",
    "        interp='RANDOM'), T.Normalize(\r\n",
    "            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\r\n",
    "])\r\n",
    "\r\n",
    "eval_transforms = T.Compose([\r\n",
    "    T.Resize(\r\n",
    "        target_size=640, interp='CUBIC'), T.Normalize(\r\n",
    "            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\r\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#数据集加载\r\n",
    "train_dataset = pdx.datasets.VOCDetection(\r\n",
    "    data_dir='VOC',\r\n",
    "    file_list='VOC/train_list.txt',\r\n",
    "    label_list='VOC/labels.txt',\r\n",
    "    transforms=train_transforms,\r\n",
    "    shuffle=True)\r\n",
    "eval_dataset = pdx.datasets.VOCDetection(\r\n",
    "    data_dir='VOC',\r\n",
    "    file_list='VOC/val_list.txt',\r\n",
    "    label_list='VOC/labels.txt',\r\n",
    "    transforms=eval_transforms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 三、模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 3.1Faster RCNN算法简介\n",
    "1. image input；\n",
    "1. 利用selective search 算法在图像中从上到下提取2000个左右的建议窗口(Region Proposal)；\n",
    "1. 将整张图片输入CNN，进行特征提取；\n",
    "1. 把建议窗口映射到CNN的最后一层卷积feature map上；\n",
    "1. 通过RoI pooling层使每个建议窗口生成固定尺寸的feature map；\n",
    "1. 利用Softmax Loss(探测分类概率) 和Smooth L1 Loss(探测边框回归)对分类概率和边框回归(Bounding box regression)联合训练.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 3.2配置超参数并训练模型\n",
    "参数说明：\n",
    "\n",
    "    train_dataset：训练数据集。\n",
    "    num_epochs：训练轮次。\n",
    "    train_batch_size：单次训练数据批次大小。\n",
    "    learning_rate：学习率。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "num_classes = len(train_dataset.labels) + 1\r\n",
    "model = pdx.det.FasterRCNN(num_classes=num_classes)\r\n",
    "model.train(\r\n",
    "    num_epochs=12,\r\n",
    "    train_dataset=train_dataset,\r\n",
    "    train_batch_size=2,\r\n",
    "    eval_dataset=eval_dataset,\r\n",
    "    learning_rate=0.0025,\r\n",
    "    lr_decay_epochs=[8, 11],\r\n",
    "    save_interval_epochs=1,\r\n",
    "    save_dir='output/faster_rcnn_r50_fpn',\r\n",
    "    use_vdl=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 四、模型评估\n",
    "效果一般，后期会进行优化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-02-16 20:08:50 [INFO]\tModel[FasterRCNN] loaded.\n",
      "2022-02-16 20:08:50 [INFO]\tStart to evaluate(total_samples=66, total_steps=66)...\n",
      "2022-02-16 20:08:53 [INFO]\tAccumulating evaluatation results...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OrderedDict([('bbox_map', 46.72785909655489)])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = pdx.load_model('output/faster_rcnn_r50_fpn/best_model')\r\n",
    "model.evaluate(eval_dataset, batch_size=1, metric=None, return_details=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 五、可视化模型效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-02-16 20:15:36 [INFO]\tModel[FasterRCNN] loaded.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0648550450>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddlex as pdx\r\n",
    "from paddlex import transforms as T\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "%matplotlib inline\r\n",
    "\r\n",
    "\r\n",
    "eval_transforms = T.Compose([\r\n",
    "    T.Resize(target_size=(1088, 1920), interp='CUBIC'), \r\n",
    "    T.Normalize(mean=[0.40158695, 0.43556893, 0.507324], std=[0.19307534, 0.19843009, 0.2915112])])\r\n",
    "\r\n",
    "model = pdx.load_model('output/faster_rcnn_r50_fpn/best_model')\r\n",
    "image_name =  'VOC/JPEGImages/260.jpeg'\r\n",
    "result = model.predict(image_name)\r\n",
    "pred = pdx.det.visualize(image_name, result, threshold=0.5, save_dir=None)\r\n",
    "pred = pred[:, :, ::-1]  # 2RGB\r\n",
    "plt.figure(figsize=(10, 10))\r\n",
    "plt.imshow(pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 作者简介\n",
    "> 百度飞桨开发者技术专家 PPDE\n",
    "\n",
    "> RHCE+RHCSA认证\n",
    "\n",
    "> 渗透测试+SRC爱好者（贡献过一些CNVD）\n",
    "\n",
    "> 微信公众号：DKsec（后期会更新啦！）\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/27ddb5b3de45496a8e59249fcd911e68d79ef079b01349ea8256c3c05c855af3)\n",
    "\n",
    "\n",
    "我在AI Studio上获得黄金等级，点亮4个徽章，来互关呀~ https://aistudio.baidu.com/aistudio/personalcenter/thirdview/314275"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
